Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (readOnly, idempotent, openWorld), description adds that default model is free, Anthropic requires a BYO API key with direct payment, and returns per-model details plus combined view.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise 4-sentence paragraph with no filler. Front-loaded with main action, then defaults, return format, and use cases. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 4 parameters (1 required), no output schema, and full schema descriptions, the description adequately explains the tool's behavior, return format (per-model score, confidence, signals, raw_response, combined view), and use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds default behavior for models, conditional requirement for _apiKey, and clarifies that context helps disambiguate. Adds value beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool probes LLMs for visibility scoring (0-100) per model, with a specific verb and resource. It distinguishes from siblings like scan_competitor_ai_presence by focusing on quantitative scoring.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains default model and optional Anthropic with BYO key. Lacks explicit alternatives or when-not-to-use, but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.